2. Bar函数

使用go.Bar()函数可以绘制柱状图以及条形图，常用参数如下：

• hoverinfo：当用户与图形互动时，鼠标指针显示的参数。
• marker：数据节点参数，包括设置颜色color、线条line、颜色模式colorscale、显示颜色条showscale、透明度opacity等。
• name：名称参数。
• opacity：透明度参数，范围是0-1。
• orientation：图形显示方向参数，v为垂直模式，h为水平模式。
• showlengend：布尔变量，用于设置图例显示参数。
• textfont：文本设置参数，包括字体、颜色和大小等。
• textposition：文本位置参数。
• text：文本参数。
• width：柱状图的条形宽度参数。
• xaxis/yaxis：x/y坐标轴参数。
• x/y：x/y轴数据参数。

在Layout中对barmode参数进行设置，可以绘制出不同类型的柱状图：

• 如果设置为stack，即为堆积柱状图 Stacked Bar；
• 如果设置成group，即为柱状簇 Grouped Bar。

2.1 柱状图 Bar

# 2-1 Basic Bar Chart 基本柱状图
import plotly.graph_objects as go

animals = ['giraffes', 'orangutans', 'monkeys', 'elephant', 'zebra']
y1 = [10,15,20,18,12]

fig = go.Figure(data=go.Bar(x=animals,y=y1))  # 设置x/y轴数据
fig.show()

# 2-2 Styled Bar Chart 风格化柱状图
import plotly.graph_objects as go

animals = ['giraffes', 'orangutans', 'monkeys', 'elephant', 'zebra']
y1 = [10,15,20,18,12]

fig = go.Figure()

fig.add_trace(go.Bar(
    x=animals, y=y1,
    width=0.6,  # 设置柱形宽度
    opacity=0.8,  # 设置不透明度
    
    hoverinfo='x+y',  # 设置鼠标指针交互显示文本
    text=y1,  # 设置显示文本
    textfont=dict(
        size=15,
        color='rgba(0,0,0,0.5)',
        family='Arial'),  # 设置显示文本的字体、颜色、大小
    textposition='inside',  # 设置显示文本的位置
    
    marker=dict(
        color='#0097a7',
        line=dict(color='#f9a825',width=1.5),
        opacity=0.8),  # 设置数据节点，包括颜色、线条、透明度等参数
))

fig.show()

数据节点参数marker是经常会使用到的参数，可以通过help(go.Bar.marker)来查看。关于marker的颜色设置：

• 固定的颜色（即将颜色传递给color参数）：颜色设定可以使用rgb()或rgba()，也可用十六进制颜色码：https://www.materialpalette.com/colors
• 可变的颜色（即将变量传递给color参数）：可以设定colorscale（颜色范围），包括:
  • 多个颜色的列表；
  • 2个元素的列表，从0到1颜色的过渡；
  • 命名的颜色，比如：Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody, Earth,Electric,Viridis,Cividis

# 思考：如何将柱形的颜色随y值的大小变化？——给color参数传递连续值

fig = go.Figure(data=go.Bar(
        x=animals,y=y1,
        marker=dict(
            color=y1,  # 将y1值传递给color参数（连续值）
            colorscale=[[0, 'white'], [1, '#E15759']],  # 设置颜色范围
            showscale=True  # 设置是否显示图例
        )))

fig.show()

# 思考：如何将y值最大的柱形颜色和宽度设置成不同？——给color参数传递离散值

# 方法1. 可以通过numpy数组中argmax()来获取最大值索引
# 方法2. 可以使用pandas对象中idxmax()来获取最大值索引

# 自定义颜色列表，并修改其中一个值
import numpy as np
colors=['#607d8b',] * 5
colors[np.array(y1).argmax()] = '#e91e63'

# 自定义宽度列表，并修改其中一个值
width=[0.6] * 5
width[np.array(y1).argmax()]= 0.9

# 另一种写法
# import pandas as pd
# colors=['#607d8b',] * 5
# colors[pd.Series(y1).idxmax()] = '#e91e63'
# width=[0.6] * 5
# width[pd.Series(y1).idxmax()]=0.9

fig = go.Figure(data=go.Bar(
        x=animals,y=y1,
        marker=dict(color=colors),  # 将自定义颜色列表传递给color参数
        width=width  # 将自定义宽度列表传递给width参数
))

fig.show()

# 2-3 Stacked Bar Chart 堆积柱状图
import plotly.graph_objects as go
animals=['giraffes', 'orangutans', 'monkeys', 'elephant', 'zebra']
y1 = [10,15,20,18,12]
y2 = [12,10,18,20,15]

fig = go.Figure()

fig.add_trace(go.Bar(x=animals, y=y1, name='SF Zoo'))  
fig.add_trace(go.Bar(x=animals, y=y2, name='LA Zoo'))  # 设置每个trace的名称

fig.update_layout(barmode='stack')  # 更改barmode为stack

fig.show()

# 2-4 Grouped Bar Chart 柱状簇
import plotly.graph_objects as go
animals = ['giraffes', 'orangutans', 'monkeys', 'elephant', 'zebra']
y1 = [10,15,20,18,12]
y2 = [12,10,18,20,15]

fig = go.Figure()

fig.add_trace(go.Bar(x=animals, y=y1, name='SF Zoo'))  
fig.add_trace(go.Bar(x=animals, y=y2, name='LA Zoo'))  # 设置每个trace的名称

fig.update_layout(
    barmode='group',  # 更改barmode为group
    bargap=0.2,  # 设置bar之间的间距
    bargroupgap=0.05,  # 设置同一个group的bar之间的间距
)

fig.show()

2.2 条形图 Horizontal Bar

# 2-5 Basic Horizontal Bar Chart
import plotly.graph_objects as go
animals=['giraffes', 'orangutans', 'monkeys', 'elephant', 'zebra']
y1=[10,15,20,18,12]
y2=[12,10,18,20,15]

fig = go.Figure()

fig.add_trace(go.Bar(y=animals, x=y1, name='SF Zoo', orientation='h'))  
fig.add_trace(go.Bar(y=animals, x=y2, name='LA Zoo', orientation='h'))

# fig.update_traces(orientation='h')  # 对于多个trace的相同设置可以通过.update_traces()方法来实现

fig.update_layout(barmode='stack')

fig.show()

# 进阶：优化Layout，可以通过help(go.Layout)查看图形布局Layout的参数

fig.update_layout(
    title='Basic Horizontal Bar Chart',  # 设置标题
    xaxis=dict(title='Number',gridcolor='rgba(0,0,0,0.2)'),  # 设置X轴
    yaxis=dict(title='Animals',),  # 设置Y轴
    plot_bgcolor='white'
)

fig.show()

练习1. 绘制水平柱状图 Profit by Sub-Category

导入数据文件'Sample - Superstore.xls'，绘制水平柱状图，展示每个商品子类别（Sub-Category）的利润（Profit），并根据利润大小显示颜色。

# Step1. 导入数据文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('./Sample - Superstore.xls',sheet_name='Orders')
df.head()

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	...	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
0	1	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420.0	South	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600	2	0.00	41.9136
1	2	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420.0	South	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,...	731.9400	3	0.00	219.5820
2	3	CA-2017-138688	2017-06-12	2017-06-16	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	...	90036.0	West	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b...	14.6200	2	0.00	6.8714
3	4	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311.0	South	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775	5	0.45	-383.0310
4	5	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311.0	South	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold 'N Roll Cart System	22.3680	2	0.20	2.5164

5 rows × 21 columns

# Step2. 数据分析：对Sub-Category进行分组，再对每组的Profit求和（数据聚合与分组操作——GroupBy机制）
data = df.groupby('Sub-Category')['Profit'].sum()
# df['Profit'].groupby(df['Sub-Category']).sum()  # 另一种写法
print(data)  # 返回Series：左侧是索引index，右侧是值values
print('\n',data.index)  # 使用.index属性查看索引
print('\n',data.values)  # 使用.values属性或者to_numpy()方法查看值（返回数组）

Sub-Category
Accessories    41936.6357
Appliances     18138.0054
Art             6527.7870
Binders        30221.7633
Bookcases      -3472.5560
Chairs         26590.1663
Copiers        55617.8249
Envelopes       6964.1767
Fasteners        949.5182
Furnishings    13059.1436
Labels          5546.2540
Machines        3384.7569
Paper          34053.5693
Phones         44515.7306
Storage        21278.8264
Supplies       -1189.0995
Tables        -17725.4811
Name: Profit, dtype: float64

 Index(['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs',
       'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels',
       'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables'],
      dtype='object', name='Sub-Category')

 [ 41936.6357  18138.0054   6527.787   30221.7633  -3472.556   26590.1663
  55617.8249   6964.1767    949.5182  13059.1436   5546.254    3384.7569
  34053.5693  44515.7306  21278.8264  -1189.0995 -17725.4811]

# Step3. 绘制水平柱状图
import plotly.graph_objects as go

fig = go.Figure(data=go.Bar(
    y=data.index,
    x=data.values,
    marker=dict(
        color=data.values,
        colorscale=[[0,'#fb8c00'],[0.25,'white'],[1,'#1565c0']],
        showscale=True),
    orientation='h'
))

fig.update_layout(
    title='Profit by Sub-Category',
    plot_bgcolor='white',  
    xaxis=dict(title='Profit',gridcolor='rgba(0,0,0,0.05)'),
    yaxis=dict(title='Sub-Category')
)

fig.show()

# 思考：如何设置一个利润阈值？柱形的颜色由是否超过阈值来决定

thred = 25000
colors=['#BAB0AC']*data.shape[0]
for i in range(data.shape[0]):
    if data[i]>thred:
        colors[i]='#E15759'

fig = go.Figure(data=go.Bar(
    y=data.index,
    x=data.values,
    marker=dict(color=colors),
    orientation='h'
))

fig.update_layout(
    title='Profit by Sub-Category',
    plot_bgcolor='white',  
    xaxis=dict(title='Profit',gridcolor='rgba(0,0,0,0.05)'),
    yaxis=dict(title='Sub-Category'),
)

fig.show()

练习2. 绘制堆积柱状图 Profit by Sub-Category & Segment

导入数据文件'Sample - Superstore.xls'，使用堆积柱状图展示每个商品子类别（Sub-Category）中，不同细分客户（Segment）的利润（Profit）。

# Step1. 导入数据文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('./Sample - Superstore.xls',sheet_name='Orders')
df.head()

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	...	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
0	1	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420.0	South	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600	2	0.00	41.9136
1	2	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420.0	South	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,...	731.9400	3	0.00	219.5820
2	3	CA-2017-138688	2017-06-12	2017-06-16	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	...	90036.0	West	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b...	14.6200	2	0.00	6.8714
3	4	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311.0	South	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775	5	0.45	-383.0310
4	5	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311.0	South	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold 'N Roll Cart System	22.3680	2	0.20	2.5164

5 rows × 21 columns

# Step2. 数据分析：从绘图角度来思考，一个图形fig中有三个trace（Segment），每个trace对应多个柱形（Sub-Category）
# 对Segment和Sub-Category两个指标进行分组，再对每组的Profit求和（数据聚合与分组操作——GroupBy机制）
data = df.groupby(['Segment','Sub-Category'])['Profit'].sum()
# df['Profit'].groupby([df['Segment'],df['Sub-Category']]).sum()  # 另一种写法
data

Segment      Sub-Category
Consumer     Accessories     20735.9225
             Appliances       6981.9282
             Art              3454.3011
             Binders         17995.5972
             Bookcases       -4435.6382
             Chairs          13235.3319
             Copiers         24083.7106
             Envelopes        3264.4126
             Fasteners         576.8008
             Furnishings      7919.4227
             Labels           3075.9884
             Machines         2141.0618
             Paper           15534.6436
             Phones          23837.1147
             Storage          7104.2004
             Supplies        -1657.5513
             Tables          -9728.0378
Corporate    Accessories     12707.4805
             Appliances       7429.8952
             Art              2004.6477
             Binders          6377.3201
             Bookcases         638.4502
             Chairs           8344.6565
             Copiers         18990.2789
             Envelopes        2571.2290
             Fasteners         251.9030
             Furnishings      3508.2077
             Labels           1760.8273
             Machines          703.0190
             Paper           10361.5468
             Phones          11766.2196
             Storage          9131.0247
             Supplies          338.9264
             Tables          -4906.4986
Home Office  Accessories      8493.2327
             Appliances       3726.1820
             Art              1068.8382
             Binders          5848.8460
             Bookcases         324.6320
             Chairs           5010.1779
             Copiers         12543.8354
             Envelopes        1128.5351
             Fasteners         120.8144
             Furnishings      1631.5132
             Labels            709.4383
             Machines          540.6761
             Paper            8157.3789
             Phones           8912.3963
             Storage          5043.6013
             Supplies          129.5254
             Tables          -3090.9447
Name: Profit, dtype: float64

# 得到的结果是一个MultiIndex（多重索引）的Series：左侧是两层索引index，右侧是值values
# data.index  # 使用index属性查看多重索引
print(data.index.levels[0])  # 查看外层索引
print(data.index.levels[1])  # 查看内层索引
print(data['Consumer'])  # 指定任一外层索引，查看内层Series

Index(['Consumer', 'Corporate', 'Home Office'], dtype='object', name='Segment')
Index(['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs',
       'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels',
       'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables'],
      dtype='object', name='Sub-Category')
Sub-Category
Accessories    20735.9225
Appliances      6981.9282
Art             3454.3011
Binders        17995.5972
Bookcases      -4435.6382
Chairs         13235.3319
Copiers        24083.7106
Envelopes       3264.4126
Fasteners        576.8008
Furnishings     7919.4227
Labels          3075.9884
Machines        2141.0618
Paper          15534.6436
Phones         23837.1147
Storage         7104.2004
Supplies       -1657.5513
Tables         -9728.0378
Name: Profit, dtype: float64

# Step3. 绘制堆积柱状图
import plotly.graph_objects as go

fig=go.Figure()

# 使用for循环来遍历segment，绘制多个trace
for i in data.index.levels[0]: 
    fig.add_trace(
        go.Bar(
            x=data[i].index,  # 当前segment对应Series的索引
            y=data[i].values,  # 当前segment对应Series的值
            name=i  # 当前segment名称
        ))
    
fig.update(layout=dict(
    title='Profit by Sub-Category & Segment',
    barmode='stack',
))

fig.show()

# 思考：如何自定义颜色？

fig=go.Figure()

colors=['#76B7B2','#BAB0AC','#FF9DA7']  # 自定义颜色列表（离散值）

# 使用for循环来遍历segment，绘制多个trace
for i,c in zip(data.index.levels[0],colors):   # 使用zip()函数同时遍历两个序列
    fig.add_trace(
        go.Bar(
            x=data[i].index,  # 当前segment对应Series的索引
            y=data[i].values,  # 当前segment对应Series的值
            name=i,  # 当前segment名称
            marker=dict(color=c)  # 将离散颜色值传递给color参数
        ))

fig.update(layout=dict(
    title='Profit by Sub-Category & Segment',
    barmode='stack',
))

fig.show()